Compass Scraper
Scrape Listings and Prices from Compass
Extract for-sale and rental listings, asking prices, price cuts, days-on-market, and agent details from one of the largest US residential real-estate brokerages. Compass sits behind Amazon CloudFront and rate-limits automated traffic. Scrape.do handles it automatically.
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12.5M Monthly Visits. Every US Market. One API.
Compass is one of the largest technology-driven residential real-estate brokerages in the United States, pairing nearly 30,000 agents across 350+ cities with a public portal of for-sale and rental listings. It draws over 12.5 million monthly visits, and its listing, pricing, and agent data make it a core source for US property-market intelligence.
With Scrape.do you can track asking prices, price cuts, and days-on-market across markets like New York, Los Angeles, and Miami, monitor new listings and status changes, capture agent and brokerage coverage, and benchmark Compass inventory against Zillow, Redfin, and Realtor.com. All through a single API call.
CloudFront WAF. Rate Limits. Handled.
Compass sits behind Amazon CloudFront, which fingerprints traffic and throttles automated requests, returning 403 responses and rate-limit blocks once it flags your IP. Repeated hits from datacenter ranges get cut off quickly, so a plain scraper stalls after a handful of pages.
Scrape.do rotates high-quality US residential IPs with real browser fingerprints, spreads requests to stay under CloudFront's rate limits, and returns clean listing and search HTML for as little as $1 per 1,000 requests. No proxy pools, no CAPTCHA solvers, no maintenance.
How to Scrape Compass
Select a Target
Send API Request
import requests
import urllib.parse
token = "<SDO-token>"
targetUrl = "https://www.compass.com/homes-for-sale/new-york-ny/"
render = "false" # set to "true" to enable JavaScript rendering
super = "false" # set to "true" to enable residential proxies
geoCode = "us"
encodedUrl = urllib.parse.quote(targetUrl)
url = f"https://api.scrape.do/?token={token}&url={encodedUrl}&render={render}&super={super}&geoCode={geoCode}"
response = requests.get(url)
print(response.text)Get HTML
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>New York, NY Homes for Sale & Real Estate | Compass</title>
</head>
<body>
<header class="searchHeader">
<h1>New York, NY Homes for Sale</h1>
<span class="resultCount">3,214 homes</span>
</header>
<ul class="listingGrid">
<li class="uc-listingCard">
<a href="/listing/145-central-park-north-new-york-ny-10026/1234567890/">
<div class="uc-listingCard-price">$1,250,000</div>
<div class="uc-listingCard-summary">
<span class="beds">2 Beds</span>
<span class="baths">2 Baths</span>
<span class="sqft">1,180 Sq Ft</span>
</div>
<div class="uc-listingCard-address">145 Central Park North, New York, NY 10026</div>
<div class="uc-listingCard-type">Condo</div>
<div class="uc-listingCard-agent">Listed by Compass</div>
<div class="uc-listingCard-dom">14 days on Compass</div>
</a>
</li>
<!-- ... remaining listing cards ... -->
</ul>
</body>
</html>
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Sounds great, but I have a few questions..
Yes. Compass sits behind Amazon CloudFront, which fingerprints traffic and rate-limits automated requests, returning 403 responses once it flags an IP. Scrape.do rotates real US residential IPs with full browser fingerprints and paces requests to stay under CloudFront's limits, so you receive clean listing and search HTML. Success rates exceed 95% on search and listing pages.
You can extract property addresses, asking prices and price cuts, number of beds and baths, square footage, property type, days on market, listing status, photos, listing descriptions, and agent and brokerage details across for-sale and rental inventory in 350+ US cities.
For most Compass search and listing pages a plain request returns the server-rendered HTML with prices and core listing fields, so render=false works and keeps costs low. If a specific page loads details client-side, add render=true so Scrape.do executes the page JavaScript in a real browser, and keep geoCode=us for accurate US market data.
